This investigation is concerned with the effects of employing a Kalman filter to estimate the states in a system for which the mathematical model is inaccurate. Consideration is given to both intentional and unintentional mis-identification of parameters in the assumed plant dynamics. An algorithm consisting of four matrix equations is derived which yields the actual covariance of estimation error when errors in the assumed model are known. Depending upon the gain sequence used, the derived equations can be used to either (1) produce optimal estimates when errors are deliberate or (2) aid in the determination of mis-identification costs in terms of filter performance degradation if the relative accuracy of parameter identification is known. Analytic examples of scalar cases are included, as well as computer simulations for specific higher order systems, including the employment of a second order filter model with a fourth order plant.
CameraCanon EOS 5D Mark II
Dc_contributor_adviserDemetry, James S.
Etd_thesisdegree_nameMaster of Science in Engineering Electronics
OcrABBYY FineReader 8.0
RightsApproved for public release, distribution unlimited